文章基本信息

标题：TopRecs+: Pushing the Envelope on Recommender Systems
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作者：Mohammad Khabbaz ; Min Xie ; Laks V.S. Lakshmanan 等
期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2011
卷号：34
期号：02
出版社：IEEE Computer Society
摘要：Spurred by the advances in collaborative filtering, by applications that form the core business of companies such as Amazon and Netflix, and indeed by incentives such as the famous Netflix Prize, research on recommender systems has become quite mature and sophisticated algorithms that enjoy high prediction accuracy have been developed [1]. Most of this research has been concerned with what we regard as first generation recommender systems. Ever since the database community got interested in recommender systems, people have begun asking questions related to functionality. This includes developing flexible recommender systems which can efficiently compute top-k items within their framework [18] and using recommender systems to design packages subject to user specified constraints [11].